





import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


_snake_case    :       Optional[Any]		       =    logging.get_logger(__name__)



class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					"""linear"""
  a_              =					"""cosine"""
  a_              =					"""cosine_with_restarts"""
  a_              =					"""polynomial"""
  a_              =					"""constant"""
  a_              =					"""constant_with_warmup"""
  a_              =					"""piecewise_constant"""





def     a_       (					lowerCAmelCase_ :     Optimizer,      lowerCAmelCase_ :     int = -1      ):
   return LambdaLR(lowerCAmelCase_,      lambda lowerCAmelCase_      : 1,      last_epoch=lowerCAmelCase_      )

def     a_       (					lowerCAmelCase_ :     Optimizer,      lowerCAmelCase_ :     int,      lowerCAmelCase_ :     int = -1      ):

   def lr_lambda(lowerCAmelCase_ :     int      ):
      if current_step < num_warmup_steps:
         return float(lowerCAmelCase_      ) / float(max(1.0,      lowerCAmelCase_      )      )
      return 1.0

   return LambdaLR(lowerCAmelCase_,      lowerCAmelCase_,      last_epoch=lowerCAmelCase_      )

def     a_       (					lowerCAmelCase_ :     Optimizer,      lowerCAmelCase_ :     str,      lowerCAmelCase_ :     int = -1      ):
   __lowerCAmelCase				      =      {}
   __lowerCAmelCase				      =      step_rules.split(','      )
   for rule_str in rule_list[:-1]:
      __lowerCAmelCase      ,      __lowerCAmelCase				      =      rule_str.split(':'      )
      __lowerCAmelCase				      =      int(lowerCAmelCase_      )
      __lowerCAmelCase				      =      float(lowerCAmelCase_      )
      __lowerCAmelCase				      =      value
   __lowerCAmelCase				      =      float(rule_list[-1]      )

   def create_rules_function(lowerCAmelCase_ :     Union[str, Any],      lowerCAmelCase_ :     List[Any]      ):
      def rule_func(lowerCAmelCase_ :     int      ) -> float:
         __lowerCAmelCase				      =      sorted(rules_dict.keys()      )
         for i, sorted_step in enumerate(lowerCAmelCase_      ):
            if steps < sorted_step:
               return rules_dict[sorted_steps[i]]
         return last_lr_multiple

      return rule_func

   __lowerCAmelCase				      =      create_rules_function(lowerCAmelCase_,      lowerCAmelCase_      )

   return LambdaLR(lowerCAmelCase_,      lowerCAmelCase_,      last_epoch=lowerCAmelCase_      )

def     a_       (					lowerCAmelCase_ :     int,      lowerCAmelCase_ :     str,      lowerCAmelCase_ :     str,      lowerCAmelCase_ :     Any=-1      ):

   def lr_lambda(lowerCAmelCase_ :     int      ):
      if current_step < num_warmup_steps:
         return float(lowerCAmelCase_      ) / float(max(1,      lowerCAmelCase_      )      )
      return max(
          0.0,      float(num_training_steps - current_step      ) / float(max(1,      num_training_steps - num_warmup_steps      )      )      )

   return LambdaLR(lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_      )

def     a_       (					lowerCAmelCase_ :     Optimizer,      lowerCAmelCase_ :     int,      lowerCAmelCase_ :     int,      lowerCAmelCase_ :     float = 0.5,      lowerCAmelCase_ :     int = -1      ):

   def lr_lambda(lowerCAmelCase_ :     Tuple      ):
      if current_step < num_warmup_steps:
         return float(lowerCAmelCase_      ) / float(max(1,      lowerCAmelCase_      )      )
      __lowerCAmelCase				      =      float(current_step - num_warmup_steps      ) / float(max(1,      num_training_steps - num_warmup_steps      )      )
      return max(0.0,      0.5 * (1.0 + math.cos(math.pi * float(lowerCAmelCase_      ) * 2.0 * progress      ))      )

   return LambdaLR(lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_      )

def     a_       (					lowerCAmelCase_ :     Optimizer,      lowerCAmelCase_ :     int,      lowerCAmelCase_ :     int,      lowerCAmelCase_ :     int = 1,      lowerCAmelCase_ :     int = -1      ):

   def lr_lambda(lowerCAmelCase_ :     str      ):
      if current_step < num_warmup_steps:
         return float(lowerCAmelCase_      ) / float(max(1,      lowerCAmelCase_      )      )
      __lowerCAmelCase				      =      float(current_step - num_warmup_steps      ) / float(max(1,      num_training_steps - num_warmup_steps      )      )
      if progress >= 1.0:
         return 0.0
      return max(0.0,      0.5 * (1.0 + math.cos(math.pi * ((float(lowerCAmelCase_      ) * progress) % 1.0)      ))      )

   return LambdaLR(lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_      )

def     a_       (					lowerCAmelCase_ :     Union[str, Any],      lowerCAmelCase_ :     Union[str, Any],      lowerCAmelCase_ :     Optional[Any],      lowerCAmelCase_ :     Optional[int]=1E-7,      lowerCAmelCase_ :     int=1.0,      lowerCAmelCase_ :     Optional[int]=-1      ):
   __lowerCAmelCase				      =      optimizer.defaults['lr']
   if not (lr_init > lr_end):
      raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""      )

   def lr_lambda(lowerCAmelCase_ :     int      ):
      if current_step < num_warmup_steps:
         return float(lowerCAmelCase_      ) / float(max(1,      lowerCAmelCase_      )      )
      elif current_step > num_training_steps:
         return lr_end / lr_init  # as LambdaLR multiplies by lr_init
      else:
         __lowerCAmelCase				      =      lr_init - lr_end
         __lowerCAmelCase				      =      num_training_steps - num_warmup_steps
         __lowerCAmelCase				      =      1 - (current_step - num_warmup_steps) / decay_steps
         __lowerCAmelCase				      =      lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_      )


_snake_case    :       Optional[int]		       =    {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}




def     a_       (					lowerCAmelCase_ :     Union[str, SchedulerType],      lowerCAmelCase_ :     Optimizer,      lowerCAmelCase_ :     Optional[str] = None,      lowerCAmelCase_ :     Optional[int] = None,      lowerCAmelCase_ :     Optional[int] = None,      lowerCAmelCase_ :     int = 1,      lowerCAmelCase_ :     float = 1.0,      lowerCAmelCase_ :     int = -1,      ):
   __lowerCAmelCase				      =      SchedulerType(lowerCAmelCase_      )
   __lowerCAmelCase				      =      TYPE_TO_SCHEDULER_FUNCTION[name]
   if name == SchedulerType.CONSTANT:
      return schedule_func(lowerCAmelCase_,      last_epoch=lowerCAmelCase_      )

   if name == SchedulerType.PIECEWISE_CONSTANT:
      return schedule_func(lowerCAmelCase_,      step_rules=lowerCAmelCase_,      last_epoch=lowerCAmelCase_      )

   # All other schedulers require `num_warmup_steps`
   if num_warmup_steps is None:
      raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument."""      )

   if name == SchedulerType.CONSTANT_WITH_WARMUP:
      return schedule_func(lowerCAmelCase_,      num_warmup_steps=lowerCAmelCase_,      last_epoch=lowerCAmelCase_      )

   # All other schedulers require `num_training_steps`
   if num_training_steps is None:
      raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument."""      )

   if name == SchedulerType.COSINE_WITH_RESTARTS:
      return schedule_func(
          lowerCAmelCase_,      num_warmup_steps=lowerCAmelCase_,      num_training_steps=lowerCAmelCase_,      num_cycles=lowerCAmelCase_,      last_epoch=lowerCAmelCase_,      )

   if name == SchedulerType.POLYNOMIAL:
      return schedule_func(
          lowerCAmelCase_,      num_warmup_steps=lowerCAmelCase_,      num_training_steps=lowerCAmelCase_,      power=lowerCAmelCase_,      last_epoch=lowerCAmelCase_,      )

   return schedule_func(
       lowerCAmelCase_,      num_warmup_steps=lowerCAmelCase_,      num_training_steps=lowerCAmelCase_,      last_epoch=lowerCAmelCase_      )


from __future__ import annotations


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->bool:




  '''simple docstring'''

  _UpperCamelCase           =    get_failure_array(a__ )

  # 2) Step through text searching for pattern
  _UpperCamelCase      ,						_UpperCamelCase           =    0, 0  # index into text, pattern
  while i < len(a__ ):
    if pattern[j] == text[i]:
      if j == (len(a__ ) - 1):
        return True
      j += 1

    # if this is a prefix in our pattern
    # just go back far enough to continue
    elif j > 0:
      _UpperCamelCase           =    failure[j - 1]
      continue
    i += 1
  return False


def 				lowerCAmelCase__       (			a__ )      ->list[int]:




  '''simple docstring'''

  _UpperCamelCase           =    [0]
  _UpperCamelCase           =    0
  _UpperCamelCase           =    1
  while j < len(a__ ):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _UpperCamelCase           =    failure[i - 1]
      continue
    j += 1
    failure.append(a__ )
  return failure


if __name__ == "__main__":
   # Test 1)
   lowerCamelCase__  =					'''abc1abc12'''
   lowerCamelCase__  =					'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
   lowerCamelCase__  =					'''alskfjaldsk23adsfabcabc'''
   assert kmp(pattern, texta) and not kmp(pattern, texta)

   # Test 2)
   lowerCamelCase__  =					'''ABABX'''
   lowerCamelCase__  =					'''ABABZABABYABABX'''
   assert kmp(pattern, text)

   # Test 3)
   lowerCamelCase__  =					'''AAAB'''
   lowerCamelCase__  =					'''ABAAAAAB'''
   assert kmp(pattern, text)

   # Test 4)
   lowerCamelCase__  =					'''abcdabcy'''
   lowerCamelCase__  =					'''abcxabcdabxabcdabcdabcy'''
   assert kmp(pattern, text)

   # Test 5)
   lowerCamelCase__  =					'''aabaabaaa'''
   assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]




